Accuracy of an Automated System for Tuberculosis Detection on Chest Radiographs in High-risk Screening

🔗2018

🔗Journal/Publication: The International Journal of Tuberculosis and Lung Disease

🔗Read it in full version: https://pubmed.ncbi.nlm.nih.gov/29663963/

Abstract

Setting: Tuberculosis (TB) screening programmes can be optimised by reducing the number of chest radiographs (CXRs) requiring interpretation by human experts.

Objective: To evaluate the performance of computerised detection software in triaging CXRs in a high-throughput digital mobile TB screening programme.

Design: A retrospective evaluation of the software was performed on a database of 38 961 postero-anterior CXRs from unique individuals seen between 2005 and 2010, 87 of whom were diagnosed with TB. The software generated a TB likelihood score for each CXR. This score was compared with a reference standard for notified active pulmonary TB using receiver operating characteristic (ROC) curve and localisation ROC (LROC) curve analyses.

Results: On ROC curve analysis, software specificity was 55.71% (95%CI 55.21-56.20) and negative predictive value was 99.98% (95%CI 99.95-99.99), at a sensitivity of 95%. The area under the ROC curve was 0.90 (95%CI 0.86-0.93). Results of the LROC curve analysis were similar.

Conclusion: The software could identify more than half of the normal images in a TB screening setting while maintaining high sensitivity, and may therefore be used for triage.